Sources and composition of outdoor air pollution and adverse health outcomes in Canada
Notice bibliographique
Résumé
Ambient air pollution, including fine particulate matter (PM2.5) and oxidant gases (ozone, nitrogen dioxide), contribute to disease outcomes such as cardiovascular and respiratory diseases and cancer through the mechanism of oxidative stress. The source of air pollution influences its composition, which in turn affects its toxicity. Recently, there has been an interest in understanding how specific constituents in particulate air pollution (including metals, sulfur) and oxidative properties, which differ depending on the source, are related to health outcomes. The overarching goal of this thesis is to fill several knowledge gaps on adverse health outcomes associated with specific sources, constituents and oxidative properties of air pollution in Canada. In Objective 1, we performed a repeated-measures panel study with 71 children to examine associations between short-term and sub-chronic PM2.5 or oxidant gases and two measures of cardiovascular health (retinal blood vessel diameter and blood pressure). The study took place in a region of Vancouver Island that is impacted by residential biomass burning. Multivariable linear mixed-effect models were used to estimate associations between outdoor air pollution (PM2.5 or oxidant gases) and cardiovascular outcomes, and interactions between PM2.5 and oxidant gases were also considered. We observed inverse associations between oxidant gases and retinal arteriolar diameter; for example, each 10 ppb increase in 7-day mean oxidant gases were associated with a 2.63 μm (95% confidence interval: -4.63, -0.63) decrease in retinal arteriolar diameter. Moreover, oxidant gases modified the associations between PM2.5 and arteriolar diameter, with weak inverse associations observed between PM2.5 and arteriolar diameter only when oxidant gases were elevated. In Objective 2, we examined whether associations between short-term PM2.5 or oxidant gases and respiratory hospitalizations were modified by metals or sulfur content in PM2.5 or particle oxidative potential in a case-crossover study of 10,500 Canadian children. Multivariable conditional logistic regression models were used to estimate associations between air pollutants and respiratory hospitalizations, above and below median values for particle metals, sulfur and oxidative potential. Lag-1 PM2.5 mass was not associated with respiratory hospitalizations in analyses ignoring particle constituents and oxidative potential, but when models were examined above and below median metals, sulfur, and oxidative potential, positive associations were observed above the median. For example, the odds ratio and 95% confidence interval per 10 μg/m3 increase in PM2.5 were 1.084 (1.007–1.167) when copper was above the median and 0.970 (0.929–1.014) when copper was below the median. Similar trends were observed for oxidant gases. In Objective 3, we investigated associations between wildfire exposure based on area burned within a 20 and 50 km radius of residential location and the incidence of lung and brain cancer, non-Hodgkin lymphoma, leukemia, and multiple myeloma among approximately 2 million participants in the 1996 Canadian Census Health and Environment Cohort. Using multivariable Cox proportional hazards models, we observed positive associations between wildfires and lung and brain cancer. For example, cohort members exposed to a wildfire within 50 km of residential locations in the past 10 years had a 4.9% (95% confidence interval: 2.8%-7.1%) relatively higher incidence of lung cancer than unexposed populations, and a 10% relatively higher incidence (95% confidence interval: 2.6%-17.9%) of brain tumours. Wildfires were not associated with haematologic cancers. Overall, these findings demonstrate adverse health effects of exposure to different sources of air pollution, including residential biomass burning and wildfires, as well as specific constituents (metals, sulfur) and oxidative properties of air pollution in the Canadian population
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».